In this work,the local structure and transport properties of three typical alkali chlorides(LiCl,NaCl,and KCl)were investigated by our newly trained deep potentials(DPs).We extracted datasets from ab initio molecular ...In this work,the local structure and transport properties of three typical alkali chlorides(LiCl,NaCl,and KCl)were investigated by our newly trained deep potentials(DPs).We extracted datasets from ab initio molecular dynamics(AIMD)calculations and used these to train and validate the DPs.Large-scale and long-time molecular dynamics simulations were performed over a wider range of temperatures than AIMD to confirm the reliability and generality of the DPs.We demonstrated that the generated DPs can serve as a powerful tool for simulating alkali chlorides;the DPs also provide results with accuracy that is comparable to that of AIMD and efficiency that is similar to that of empirical potentials.The partial radial distribution functions and angle distribution functions predicted using the DPs are in close agreement with those derived from AIMD.The estimated densities,self-diffusion coefficients,shear viscosities,and electrical conductivities also matched well with the AIMD and experimental data.This work provides confidence that DPs can be used to explore other systems,including mixtures of chlorides or entirely different salts.展开更多
Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding a...Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures.In this work,we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa.We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm,an active-learning deep neural network potential,and molecular dynamics simulations with ab initio accuracy.Among the 131,481 predicted structures,we successfully identify all experimentally known ice phases within the target pressure range,including the particularly challenging ice IV and V.These phases feature highly intricate H-bond networks,which have hindered previous efforts to fully explore ice structures.Additionally,we identify 34 new ice polymorphs that are potential candidates for experimental discovery.Notably,we predict the existence of a new stable ice phase,ice L,within the temperature range of 253–291 K and pressure range of 0.38–0.57 GPa,exhibiting a unique topology unseen in any known crystals.Our findings highlight the potential for experimental discovery of new ice phases.Furthermore,our approach can be applied to other complex systems,particularly those with network structures.展开更多
Understanding the microscopic ionic structure and thermal properties of the NaCl-CaCl_(2) mixture is of great importance for improving its photothermal energy conversion efficiency.However,the measured values of therm...Understanding the microscopic ionic structure and thermal properties of the NaCl-CaCl_(2) mixture is of great importance for improving its photothermal energy conversion efficiency.However,the measured values of thermophysical parameters are affected by the processes near the phase transition temperature,and the measured values often change abruptly.Classical and first-principles molecular dynamics studies have recently been performed to determine the thermal properties of molten salts,but such simulations for binary molten salts including NaCl-CaCl_(2) are still rare and limited to a range above the phase transition temperature(786.0 K),and the deviations from the measurements are still large.In this study,the molecular dynamics method based on the trained deep potential is used to systematically predict the variations of the ionic structure,phonon density of state,density and thermophysical properties including heat capacity,thermal conductivity,and diffusivity,and Prandtl number of the binary chloride system of NaCl-CaCl_(2) in a wide temperature range(600-1000 K)above the phase transition temperature.The variations and correlations of the properties(especially thermal diffusivity and Prandtl number)with temperature are deduced.It is found that an increase in temperature enhances ionic vibration,thus increasing the specific heat capacity.An increase in temperature weakens the interaction and vibrational transfer between ions,and hence the thermal conductivity tends to decrease.As the temperature increases,the heat capacity increases,while the density,thermal conductivity,thermal diffusion coefficient,and Prandtl number of the system all decrease.In general,the properties obtained by applying the deep potential trained in this work reflect the experimental values more accurately than the classical and first-principles molecular dynamics simulations.展开更多
The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La a...The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.展开更多
The interfacial proton transfer(PT)reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting,dehydrogenation,and hydrogen storage.The investiga...The interfacial proton transfer(PT)reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting,dehydrogenation,and hydrogen storage.The investigation of the PT process,in terms of thermodynamics and kinetics,has received considerable attention,but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking.Here,by applying a combination of ab initio and deep potential molecular dynamics methods,we have studied interfacial PT mechanisms by selecting the rutile SnO_(2)(110)/H_(2)O interface as an example of an oxide with the characteristic of frequently interfacial PT processes.Three types of PT pathways among the interfacial groups are found,i.e.,proton transfer from terminal adsorbed water to bridge oxygen directly(surface-PT)or via a solvent water(mediated-PT),and proton hopping between two terminal groups(adlayer PT).Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water,leading to the lowest energy barrier and the fastest relative PT rate.In particular,it is found that the full solvation environment plays a crucial role in water-mediated proton conduction,while having little effect on direct PT reactions.The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO_(2),TiO_(2),and IrO_(2).Consequently,this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface,as well as the PT mechanisms at such oxide/liquid interfaces,which can help understand the important chemical processes in electrochemistry,photoelectrocatalysis,colloid science,and geochemistry.展开更多
Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of dis...Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).展开更多
The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles.However,the substance that has the highest melting point(Tm)keeps a secret,since precise measu...The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles.However,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly difficult.In the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure carbides.The solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting point.The highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary system.Dominating mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less stable.The improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.展开更多
To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and be...To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.展开更多
Within the mean-field model, the coherent matter waves of a dipolar condensate in a harmonic potentiM super- imposed to a deep lattice are investigated by the variational princip]e. It is shown that, in a harmonic pot...Within the mean-field model, the coherent matter waves of a dipolar condensate in a harmonic potentiM super- imposed to a deep lattice are investigated by the variational princip]e. It is shown that, in a harmonic potential superimposed to a deep lattice, it is possible to control the decoherence of Bloch oscillations due to the fact that the on-site and the inter-site dipolar interactions can not only damp out Bloch oscillations but also maintain long-lived Bloch oscillations under the certain condition. In particular, long-lived Bloch oscillations of dipolar condensate can be realized when the dipolar interaction, the contact interaction, the frequency of the harmonic potentiM and initial width of the wave packet satisfy an analytical condition. Thus the decoherence of Bloch os- cillation can be controlled by adjusting the dipolar interaction, the contact interaction, the frequency of harmonic potentiM and the initial width of the wave packet.展开更多
Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lack...Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lacking.Herein,we developed a deep learning-based interatomic potential to perform molecu-lar dynamics(MD)simulations to study the martensitic phase transformation across a range of carbon(C)concentrations.The results revealed that an increased C concentration leads to a suppressed phase boundary movement and a decelerated phase transformation rate.To overcome the timescale limitations inherent in MD simulations,metadynamics sampling was employed to accelerate the simulations of C dif-fusion.We found that C atoms tend to cluster at distances equivalent to the lattice parameter of Fe with the same sublattice occupation,leading to local lattice tetragonality.Such C-ordered structures effectively inhibit dislocation movement and enhance strength.The stress field induced by dislocations facilitates a higher degree of ordering,and the formation of C-ordered structures was identified as a potentially cru-cial strengthening mechanism for martensitic steels.The consistency between our simulation results and reported experimental observations underscores the effectiveness of the developed DP model in simu-lating martensitic phase transformation in Fe-C alloys,providing detailed insights into the mechanisms underlying this process.This work not only advances the understanding of martensitic phase transforma-tions in Fe-C alloys but also establishes a powerful computational framework for designing steels with optimized mechanical properties through the precise control of carbon ordering and dislocation behavior.展开更多
Lead-free K_(x)Na_(1-x)NbO_(3)(KNN)perovskites have garnered increasing attention due to their exceptional ferropiezoelectric properties,which are effectively tuned via polymorphic structures and domain dynamics.Howev...Lead-free K_(x)Na_(1-x)NbO_(3)(KNN)perovskites have garnered increasing attention due to their exceptional ferropiezoelectric properties,which are effectively tuned via polymorphic structures and domain dynamics.However,atomic insights into the underlying nanomechanisms governing the ferroelectricity of KNNs amidst varying factors such as composition,phase,and domain are still imperative.Here,we perform molecular dynamics simulations of phase transitions and domain dynamics for KNNs with various K/Na ratios(x=0.25∼1.0)by using ab-initio accuracy deep learning potential(DP).As a demonstration of its transferability,the newly developed DP model shows quantum accuracy in terms of the equation of states,elastic constants,and phonon dispersion relations for various KNbO_(3)and K_(0.5)Na_(0.5)NbO_(3).Furthermore,intricate temperature-dependent phase transitions and domain formation of KNNs are extensively and quantitatively captured.Simulations indicate that for KNNs with compositions x ranging from 0.25 to 1.0,the paraelectric-to-ferroelectric phase transition of KNNs is driven primarily by the order-disorder effect,while the displacive effect is dominant in the subsequent ferroelectric phase transitions.Specifically,flux-closure or herringbone-like nanodomain patterns arranged with 90°domain walls formed close to the experimental observations.Detailed analyses reveal that favorable 90°domain wall formation becomes more challenging with increasing Na content due to distinct oxygen octahedron distortion arising from the different ionic radii of K/Na atoms.It is envisioned that the combination of unified DP and atomistic simulations will help offer a robust solution for more accurate and efficient in silico explorations of complex structural,thermodynamic,and ferroelectric properties for relevant energy storage and conversion materials.展开更多
Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed...Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed so far.Here,we first develop a deep learning potential for B_(12)P_(2) based on quantum mechanical calculations.Then the isotropic lattice thermal conductivity(LTC)of crystalline B_(12)P_(2) is predicted to be 39.70±4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential.The LTC exhibits the relationship ofκL~1/T in the temperature range of 300~1500 K.More important,a twin boundary strategy is proposed to reduce the LTC of B_(12)P_(2).In nanotwinned B_(12)P_(2),the phonon transport in all directions is significantly suppressed by twin boundaries(TBs)with the isotropic LTC of 15.85±2.70 W/m⋅K,especially in the direction normal to the TB plane.The decrease of vibrational density of states and phonon participation ratio due to TBs’phonon scattering is the main reason of the low LTC in nanotwinned B_(12)P_(2).In addition,the elastic moduli(B and G)of B_(12)P_(2) crystal decrease by less than 7%after inducing TBs,which suggests that the mechanical properties are not significantly affected by TBs.Overall,this work enriches our understanding of the thermal properties of B_(12)P_(2) and offers a promising approach,i.e.,introducing TBs,to design high-performance thermoelectric materials.展开更多
Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervent...Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses.展开更多
基金the financial support provided by the National Natural Science Foundation of China(Grant U1407202 and Grant U1407126)。
文摘In this work,the local structure and transport properties of three typical alkali chlorides(LiCl,NaCl,and KCl)were investigated by our newly trained deep potentials(DPs).We extracted datasets from ab initio molecular dynamics(AIMD)calculations and used these to train and validate the DPs.Large-scale and long-time molecular dynamics simulations were performed over a wider range of temperatures than AIMD to confirm the reliability and generality of the DPs.We demonstrated that the generated DPs can serve as a powerful tool for simulating alkali chlorides;the DPs also provide results with accuracy that is comparable to that of AIMD and efficiency that is similar to that of empirical potentials.The partial radial distribution functions and angle distribution functions predicted using the DPs are in close agreement with those derived from AIMD.The estimated densities,self-diffusion coefficients,shear viscosities,and electrical conductivities also matched well with the AIMD and experimental data.This work provides confidence that DPs can be used to explore other systems,including mixtures of chlorides or entirely different salts.
基金supported by the National Natural Science Foundation of China(grant nos.12374217,92370118,and 22003050)the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(grant no.2024-ZD-01)+2 种基金the Fundamental Research Funds for the Central Universities,China.The calculations were supported by the National Supercomputing Center in Xi’an,Chinathe BohriumCloud platformat DP technologythe International Center for Materials Discovery(ICMD)cluster of NPU。
文摘Ice,a ubiquitous substance in nature,exhibits diverse forms under varying temperature and pressure conditions.However,our understanding of ice polymorphs remains incomplete.The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures.In this work,we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa.We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm,an active-learning deep neural network potential,and molecular dynamics simulations with ab initio accuracy.Among the 131,481 predicted structures,we successfully identify all experimentally known ice phases within the target pressure range,including the particularly challenging ice IV and V.These phases feature highly intricate H-bond networks,which have hindered previous efforts to fully explore ice structures.Additionally,we identify 34 new ice polymorphs that are potential candidates for experimental discovery.Notably,we predict the existence of a new stable ice phase,ice L,within the temperature range of 253–291 K and pressure range of 0.38–0.57 GPa,exhibiting a unique topology unseen in any known crystals.Our findings highlight the potential for experimental discovery of new ice phases.Furthermore,our approach can be applied to other complex systems,particularly those with network structures.
基金supported by the National Natural Science Foundation of China(Grant No.51876058 and No.52376053).
文摘Understanding the microscopic ionic structure and thermal properties of the NaCl-CaCl_(2) mixture is of great importance for improving its photothermal energy conversion efficiency.However,the measured values of thermophysical parameters are affected by the processes near the phase transition temperature,and the measured values often change abruptly.Classical and first-principles molecular dynamics studies have recently been performed to determine the thermal properties of molten salts,but such simulations for binary molten salts including NaCl-CaCl_(2) are still rare and limited to a range above the phase transition temperature(786.0 K),and the deviations from the measurements are still large.In this study,the molecular dynamics method based on the trained deep potential is used to systematically predict the variations of the ionic structure,phonon density of state,density and thermophysical properties including heat capacity,thermal conductivity,and diffusivity,and Prandtl number of the binary chloride system of NaCl-CaCl_(2) in a wide temperature range(600-1000 K)above the phase transition temperature.The variations and correlations of the properties(especially thermal diffusivity and Prandtl number)with temperature are deduced.It is found that an increase in temperature enhances ionic vibration,thus increasing the specific heat capacity.An increase in temperature weakens the interaction and vibrational transfer between ions,and hence the thermal conductivity tends to decrease.As the temperature increases,the heat capacity increases,while the density,thermal conductivity,thermal diffusion coefficient,and Prandtl number of the system all decrease.In general,the properties obtained by applying the deep potential trained in this work reflect the experimental values more accurately than the classical and first-principles molecular dynamics simulations.
基金financially supported by the National Key R &D Program of China (No.2022YFB3709300)。
文摘The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.
基金funding from the National Science Fund for Distinguished Young Scholars(Grant No.22225302)the National Natural Science Foundation of China(Grant Nos.92161113,21991151,21991150,and 22021001)+5 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.20720220008,20720220009,20720220010)Laboratory of AI for Electrochemistry(AI4EC)IKKEM(Grant Nos.RD2023100101 and RD2022070501)M.J.greatly appreciates the financial support from the Natural Science Foundation of Henan Province(Grant No.242300420420570)the Key Scientific Research Projects of Colleges and Universities in Henan Province(No.24A150031)the International Scientific and Technological Cooperation Projects in Henan Province(No.232102520020)。
文摘The interfacial proton transfer(PT)reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting,dehydrogenation,and hydrogen storage.The investigation of the PT process,in terms of thermodynamics and kinetics,has received considerable attention,but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking.Here,by applying a combination of ab initio and deep potential molecular dynamics methods,we have studied interfacial PT mechanisms by selecting the rutile SnO_(2)(110)/H_(2)O interface as an example of an oxide with the characteristic of frequently interfacial PT processes.Three types of PT pathways among the interfacial groups are found,i.e.,proton transfer from terminal adsorbed water to bridge oxygen directly(surface-PT)or via a solvent water(mediated-PT),and proton hopping between two terminal groups(adlayer PT).Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water,leading to the lowest energy barrier and the fastest relative PT rate.In particular,it is found that the full solvation environment plays a crucial role in water-mediated proton conduction,while having little effect on direct PT reactions.The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO_(2),TiO_(2),and IrO_(2).Consequently,this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface,as well as the PT mechanisms at such oxide/liquid interfaces,which can help understand the important chemical processes in electrochemistry,photoelectrocatalysis,colloid science,and geochemistry.
基金Project supported by the Joint Fund of the National Natural Science Foundation of China–“Ye Qisun”Science Fund(Grant No.U2341251)。
文摘Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).
基金supports by the National Natural Science Foundation of China(Nos.52032002,51972081,and U2130103)University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.UNPYSCT-2020052)+1 种基金Heilongjiang Touyan Team Programsupported by Bohrium Cloud Platform of DP Technology.
文摘The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles.However,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly difficult.In the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure carbides.The solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting point.The highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary system.Dominating mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less stable.The improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.
基金T W and D J S gratefully acknowledge the support of the Research Grants Council,Hong Kong SAR,through the Collaborative Research Fund Project No.8730054The work of H W is supported by the National Science Foundation of China under Grant Nos.11871110 and 12122103The work of W E is supported in part by a gift from iFlytek to Princeton University。
文摘To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11274255 and 11305132the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grand No 20136203110001+1 种基金the Natural Science Foundation of Gansu Province under Grant No 2011GS04358the Creation of Science and Technology of Northwest Normal University under Grant Nos NWNU-KJCXGC-03-48 and NWNU-LKQN-12-12
文摘Within the mean-field model, the coherent matter waves of a dipolar condensate in a harmonic potentiM super- imposed to a deep lattice are investigated by the variational princip]e. It is shown that, in a harmonic potential superimposed to a deep lattice, it is possible to control the decoherence of Bloch oscillations due to the fact that the on-site and the inter-site dipolar interactions can not only damp out Bloch oscillations but also maintain long-lived Bloch oscillations under the certain condition. In particular, long-lived Bloch oscillations of dipolar condensate can be realized when the dipolar interaction, the contact interaction, the frequency of the harmonic potentiM and initial width of the wave packet satisfy an analytical condition. Thus the decoherence of Bloch os- cillation can be controlled by adjusting the dipolar interaction, the contact interaction, the frequency of harmonic potentiM and the initial width of the wave packet.
基金the National Key Research and Devel-opment Program of China(No.2022YFB3709000)the National Natural Science Foundation of China(Nos.52101019,52122408,52071023,52474397)+1 种基金support from the Fundamental Research Funds for the Central Universities(University of Science and Technology Beijing,No.FRF-TP-2021-04C1,and 06500135)supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.
文摘Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lacking.Herein,we developed a deep learning-based interatomic potential to perform molecu-lar dynamics(MD)simulations to study the martensitic phase transformation across a range of carbon(C)concentrations.The results revealed that an increased C concentration leads to a suppressed phase boundary movement and a decelerated phase transformation rate.To overcome the timescale limitations inherent in MD simulations,metadynamics sampling was employed to accelerate the simulations of C dif-fusion.We found that C atoms tend to cluster at distances equivalent to the lattice parameter of Fe with the same sublattice occupation,leading to local lattice tetragonality.Such C-ordered structures effectively inhibit dislocation movement and enhance strength.The stress field induced by dislocations facilitates a higher degree of ordering,and the formation of C-ordered structures was identified as a potentially cru-cial strengthening mechanism for martensitic steels.The consistency between our simulation results and reported experimental observations underscores the effectiveness of the developed DP model in simu-lating martensitic phase transformation in Fe-C alloys,providing detailed insights into the mechanisms underlying this process.This work not only advances the understanding of martensitic phase transforma-tions in Fe-C alloys but also establishes a powerful computational framework for designing steels with optimized mechanical properties through the precise control of carbon ordering and dislocation behavior.
基金supported by the National Key Research and Development Program of China(2021YFB3703100 and 2023YFB3812200)the National Natural Science Foundation of China(52202066)+1 种基金the Joint Fund of Ministry of Education for Preresearch of Equipment(8091B032105)the Fundamental Research Funds for the Central Universities(2020-YB-008)。
文摘Lead-free K_(x)Na_(1-x)NbO_(3)(KNN)perovskites have garnered increasing attention due to their exceptional ferropiezoelectric properties,which are effectively tuned via polymorphic structures and domain dynamics.However,atomic insights into the underlying nanomechanisms governing the ferroelectricity of KNNs amidst varying factors such as composition,phase,and domain are still imperative.Here,we perform molecular dynamics simulations of phase transitions and domain dynamics for KNNs with various K/Na ratios(x=0.25∼1.0)by using ab-initio accuracy deep learning potential(DP).As a demonstration of its transferability,the newly developed DP model shows quantum accuracy in terms of the equation of states,elastic constants,and phonon dispersion relations for various KNbO_(3)and K_(0.5)Na_(0.5)NbO_(3).Furthermore,intricate temperature-dependent phase transitions and domain formation of KNNs are extensively and quantitatively captured.Simulations indicate that for KNNs with compositions x ranging from 0.25 to 1.0,the paraelectric-to-ferroelectric phase transition of KNNs is driven primarily by the order-disorder effect,while the displacive effect is dominant in the subsequent ferroelectric phase transitions.Specifically,flux-closure or herringbone-like nanodomain patterns arranged with 90°domain walls formed close to the experimental observations.Detailed analyses reveal that favorable 90°domain wall formation becomes more challenging with increasing Na content due to distinct oxygen octahedron distortion arising from the different ionic radii of K/Na atoms.It is envisioned that the combination of unified DP and atomistic simulations will help offer a robust solution for more accurate and efficient in silico explorations of complex structural,thermodynamic,and ferroelectric properties for relevant energy storage and conversion materials.
文摘Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed so far.Here,we first develop a deep learning potential for B_(12)P_(2) based on quantum mechanical calculations.Then the isotropic lattice thermal conductivity(LTC)of crystalline B_(12)P_(2) is predicted to be 39.70±4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential.The LTC exhibits the relationship ofκL~1/T in the temperature range of 300~1500 K.More important,a twin boundary strategy is proposed to reduce the LTC of B_(12)P_(2).In nanotwinned B_(12)P_(2),the phonon transport in all directions is significantly suppressed by twin boundaries(TBs)with the isotropic LTC of 15.85±2.70 W/m⋅K,especially in the direction normal to the TB plane.The decrease of vibrational density of states and phonon participation ratio due to TBs’phonon scattering is the main reason of the low LTC in nanotwinned B_(12)P_(2).In addition,the elastic moduli(B and G)of B_(12)P_(2) crystal decrease by less than 7%after inducing TBs,which suggests that the mechanical properties are not significantly affected by TBs.Overall,this work enriches our understanding of the thermal properties of B_(12)P_(2) and offers a promising approach,i.e.,introducing TBs,to design high-performance thermoelectric materials.
基金National Thousand Young Talents Program of China,and the National Natural Science Foundation of China(NSFC 52201180).
文摘Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses.
